Reading Screens from Wall Reflections: Coalesced Evidential Representation Learning for Hidden Screen Content Recovery

Recovering electronic screen content from wall reflections exposes a practical optical side channel for hidden-screen observation. We develop a passive hidden-screen recovery framework that reconstructs screen content using only wall light spots captured by an ordinary camera and confronts three core challenges: diffuse reflection entangles screen structure with wall appearance and imaging noise; incomplete optical evidence causes reconstruction states to drift from the underlying screen layout; and locally plausible inversions produce globally inconsistent content. To address these challenges, we propose Coalesced Evidential Representation Learning (CERL), which coalesces complementary optical cues throughout reconstruction to recover screen structure from mixed wall reflections. CERL integrates Optical Evidence Representation Learning (OERL) to organize broad luminance, relative variation, boundaries, cross-scale changes, and local contrast; Inverse State Transport Learning (ISTL) to anchor progressive reconstruction to the observed evidence; and a Collective Consistency Module (CCM) to reconcile competing local corrections into a coherent screen estimate. Evaluated across five content categories, CERL surpasses a broad range of mainstream reconstruction methods across perceptual, structural, and pixel-level metrics. This consistent performance advantage validates that CERL effectively addresses the core challenges of this passive optical side-channel problem and enables recovery of hidden screen content from ordinary wall reflections.